Stream基本操作：

Collection中提供了此接口的实例化方法：default Stream<E> stream()
Stream的主要功能是对数据进行分析处理，尤其是集合中数据的分析操作。

代码示例：

显示元素个数：

  1. public class Demo2 {
  2.     public static void main(String[] args) {
  3.         List<String> list = new ArrayList<>();
  4.         Collections.addAll(list, "Hello", "Word", "Happy", "Hi", "Has");
  5.         Stream<String> stream = list.stream();
  6.         //元素个数
  7.         System.out.println(stream.count());
  8.     }
  9. }

outPut：
5

filter:

Stream<T> filter(Predicate<? super T> predicate);
采用了断言式的函数式接口

  1. public class Demo2 {
  2.     public static void main(String[] args) {
  3.         List<String> list = new ArrayList<>();
  4.         Collections.addAll(list, "Hello", "Word", "Happy", "Hi", "Has");
  5.         Stream<String> stream = list.stream();
  6.         //有几个是含有字母H的
  7.         System.out.println(stream.filter((ele) -> ele.toUpperCase().contains("H")).count());
  8.     }
  9. }

outPut：
4

数据采集：

  1. public class Demo2 {
  2.     public static void main(String[] args) {
  3.         List<String> list = new ArrayList<>();
  4.         Collections.addAll(list, "Hello", "Word", "Happy", "Hi", "Has");
  5.         Stream<String> stream = list.stream();
  6.         //将满足条件的数据收集变为List集合
  7.         List<String> list1=stream.filter((ele) -> ele.toUpperCase().contains("H")).collect(Collectors.toList());
  8.         System.out.println(list1);
  9.     }
  10. }

outPut：
[Hello, Happy, Hi, Has]

分页处理：

  1. public class Demo2 {
  2.     public static void main(String[] args) {
  3.         List<String> list = new ArrayList<>();
  4.         Collections.addAll(list, "Hello", "Word", "Happy", "Hi", "Has");
  5.         Stream<String> stream = list.stream();
  6.         //将满足条件的数据收集变为List集合
  7.         //限制最多3个，跳过一个
  8.         List<String> list1=stream.filter((ele) -> ele.toUpperCase().contains("H")).limit(3).skip(1).collect(Collectors.toList());
  9.         System.out.println(list1);
  10.     }
  11. }

MapReduce：

分为两部分，Map处理部分，Reduce分析部分。

代码结构大致如下：

  1.  java.util.stream.Stream;
  3. class People{
  4.     String name;
  5.     int price;
  6.     People(String name,int price){
  7.         this.name=name;
  8.         this.price=price;
  9.     }
  10.     public void setName(String name) {
  11.         this.name = name;
  12.     }
  14.     public void setPrice(int price) {
  15.         this.price = price;
  16.     }
  18.     public String getName() {
  19.         return name;
  20.     }
  22.     public int getPrice() {
  23.         return price;
  24.     }
  25. }
  26. public class Demo2 {
  27.     public static void main(String[] args) {
  28.         List<People> list = new ArrayList<>();
  29.         list.add(new People("张二三",10000));
  30.         list.add(new People("李二四",20000));
  31.         list.add(new People("王二五",30000));
  32.         list.add(new People("赵二六",40000));
  33.         list.add(new People("王二麻子",50000));
  34.         LongSummaryStatistics longSummaryStatistics = list.stream().filter((ele) -> ele.getName().contains("二")).mapToLong((price) -> price.getPrice() * price.getPrice()).summaryStatistics();
  35.         System.out.println(longSummaryStatistics.getMax());
  36.         System.out.println(longSummaryStatistics.getAverage());
  37.         System.out.println(longSummaryStatistics.getCount());
  38.         System.out.println(longSummaryStatistics.getMin());
  39.         System.out.println(longSummaryStatistics.getSum());
  41.     }
  42. }

这些大数据只是JDK本身提供的支持。